littlebook The Little Book of Valuation

Young growth companies – Value Drivers

Revenue Growth

For a young, growth company to become valuable, small revenues have to become big revenues. To make judgments on revenue growth in the future, we have to assess two variables:

a.     Potential market for the product/service:  The first step in deriving the revenues for the firm is estimating the total potential market for its products and services. There are two challenges we face at this juncture.

                                               i.     Defining the product/service offered by the firm: If the product or service offered by the firm is defined narrowly, the potential market will be circumscribed by that definition and will be smaller. If we use a broader definition, the market will expand to fit that definition. For example, defining Amazon.com as a book retailer, which is what it was in 1998, would have yielded a total market of less than $ 10 billion in that year, representing total book retailing sales in 1998. Categorizing Amazon.com as a general retailer would have yielded a much larger potential market. While that might have been difficult to defend in 1998, it did become more plausible as Amazon expanded its offerings in 1999 and 2000.

                                              ii.     Estimating the market size: Having defined the market, we face the challenge of estimating the size of that market. For a product or service that is entering an established market, the best sources of data tend to be trade publications and professional forecasting services. Almost every business has a trade group that tracks the operating details of that business; there are almost 7600 trade groups just in the United States, tracking everything from aerospace to telecom.[1] In many businesses, there are firms that specialize in collecting information about the businesses for commercial and consulting purposes. For instance, the Gartner Group collects and provides data on different types of information technology business, including software.

                                            iii.     Evolution in total market over time: Since we have to forecast revenues into the future, it would be useful to get a sense of how the total market is expected to change or grow over time. This information is usually also usually available from the same sources that provide the numbers for the current market size.

b.     Market share: Once we have a sense of the overall market size and how it will changeover time, we have to estimate the share of that market that will be captured by the firm being analyzed, both in the long term and in the time periods leading up to steady state. Clearly, these estimates will depend both on the quality of the product or service that is being offered and how well it measures up against the competition. A useful exercise in estimation is to list the largest players in the targeted market currently and to visualize where the firm being valued will end up, once it has an established market. However, there are two other variables that have to be concurrently considered. One is the capacity of the management of the young company to deliver on its promises; many entrepreneurs have brilliant ideas but do not have the management and business skills to take it to commercial fruition. That is part of the reason that venture capitalists look for entrepreneurs who have had a track record of success in the past. The other is the resources that the young company can draw on to get its product/service to the desired market share.  Optimistic forecasts for market share have to be coupled with large investments in both capacity and marketing; products usually don't produce and sell themselves.

Target Operating Margin

Revenues may be the top line but as investors, but a firm can have value only if it ultimately delivers earnings. Consequently, the next step is estimating the operating expenses associated with the estimated revenues. We are stymied in this process, with young companies, both by the absence of history and the fact that these firms usually have very large operating losses at the time of the estimate. Again, we would separate the estimation process into two parts. In the first part, we would focus on estimating the operating margin in steady state, primarily by looking at more established companies in the business. Once we have the target margin, we can then look at how we expect the margin to evolve over time; this “pathway to profitability” can be rockier for some firms than others, with fixed costs and competition playing significant roles in the estimation. One final issue that has to be confronted at this stage is the level of detail that we want to build into our forecasts. In other words, should we just estimate the operating margin and profit or should we try to forecast individual operating expense items  such as labor, materials, selling and advertising expenses? As a general rule, the level of detail should decrease as we become more uncertain about a firm’s future. While this may seem counter intuitive, detail in forecasts leads to better estimates of value, if an only if we bring some information into that detail that otherwise would be missed. An analyst who has a tough time forecasting revenues in year 1 really is in no position to estimate labor or advertising costs in year 5 and should not even try. In valuing young companies, less (detail) is often more (precision).

Survival

Many young firms succumb to the competitive pressures of the market place and don’t make it. The probability of failure can be assessed in one of three ways.

c.      Sector averages: Earlier in the chapter we noted a study by Knaup and Piazza (2007) that used data from the Bureau of Labor Statistics to estimate the probability of survival for firms in different sectors from 1998 to 2005. We could use the sector averages from this study as the probability of survival for individual firms in the sector.

d.     Probits: A more sophisticated way to estimate the probability of failure is to look at firms that have succeeded and failed over a time period (say, the last 10 years) and to then try to build a model that can predict the probability of a firm failing as a function of firm specific characteristics – the cash holdings of the firm, the age and history of its founders, the business it is in and the debt that it owes.

e.     Simulations: In chapter 3, we noted that simulations can be put to good use, when confronted with uncertainty. If we can specify probability distributions (rather than just expected values) for revenues, margins and costs, we may be able to specify the conditions under which the firm will face failure (costs exceed revenues by more than 30% and debt payments coming due, for example) and estimate the probability of failure.



[1] Wikipedia has an excellent listing of industry trade groups, with links to each one. (http://en.wikipedia.org/wiki/List_of_industry_trade_groups_in_the_United_States)